| _version_ | 1866902013883711488 |
|---|---|
| author | Reyes, Karla Miriam Zapata, Gloria Heydari, Maryam Villalba-Mora, Elena Imbert, Ricardo |
| author_facet | Reyes, Karla Miriam Zapata, Gloria Heydari, Maryam Villalba-Mora, Elena Imbert, Ricardo |
| contents | <p>This study presents the design and pilot evaluation of a computer vision–based system for monitoring exercise performance in older adults, aiming to reduce frailty-related risks without the need for wearable sensors. Using MediaPipe and OpenCV, the system tracks posture and movement in real time and provides feedback on exercise execution. A pilot test was conducted with 14 volunteers performing seven exercises from the Vivifrail Spanish program (Wheel A).</p> <p>Performance was evaluated using performance analysis, yielding recognition rates between 91.06% and 100% across exercises. While the system showed high accuracy in detecting posture and repetitions, challenges such as camera positioning, clothing variability, and the absence of validation in the target population remain. These findings demonstrate the feasibility of computer vision for exercise monitoring and support its potential as an accessible tool for fall prevention and functional assessment in older adults.</p> <p>Future work will focus on clinical validation and integration into mobile platforms for home-based use. This approach will allow the older population to perform the exercises from the Vivifrail program effectively, while professionals, such as physiotherapists and geriatricians, can monitor their progress remotely and adjust the program as needed.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_4028_p-P2KnGL |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Design of an Artificial Vision System for Exercise Monitoring in Healthy Aging Reyes, Karla Miriam Zapata, Gloria Heydari, Maryam Villalba-Mora, Elena Imbert, Ricardo Biomechanics Computer vision Exercise Monitoring Accidental Falls/prevention & control Healthy Aging <p>This study presents the design and pilot evaluation of a computer vision–based system for monitoring exercise performance in older adults, aiming to reduce frailty-related risks without the need for wearable sensors. Using MediaPipe and OpenCV, the system tracks posture and movement in real time and provides feedback on exercise execution. A pilot test was conducted with 14 volunteers performing seven exercises from the Vivifrail Spanish program (Wheel A).</p> <p>Performance was evaluated using performance analysis, yielding recognition rates between 91.06% and 100% across exercises. While the system showed high accuracy in detecting posture and repetitions, challenges such as camera positioning, clothing variability, and the absence of validation in the target population remain. These findings demonstrate the feasibility of computer vision for exercise monitoring and support its potential as an accessible tool for fall prevention and functional assessment in older adults.</p> <p>Future work will focus on clinical validation and integration into mobile platforms for home-based use. This approach will allow the older population to perform the exercises from the Vivifrail program effectively, while professionals, such as physiotherapists and geriatricians, can monitor their progress remotely and adjust the program as needed.</p> |
| title | Design of an Artificial Vision System for Exercise Monitoring in Healthy Aging |
| topic | Biomechanics Computer vision Exercise Monitoring Accidental Falls/prevention & control Healthy Aging |
| url | https://doi.org/10.4028/p-P2KnGL |